Merge pull request #32 from LCOGT/feature/normalization

Adds normalization operation and corrects typo

datalab/datalab_session/data_operations/data_operation.py

@@ -53,7 +53,7 @@ def operate(self):
         """
 
     def perform_operation(self):
-        """ The generic method to perform perform the operation if its not in progress """
+        """ The generic method to perform the operation if its not in progress """
         status = self.get_status()
         if status == 'PENDING' or status == 'FAILED':
             self.set_status('IN_PROGRESS')

datalab/datalab_session/data_operations/normalization.py

@@ -0,0 +1,66 @@
+import logging
+
+import numpy as np
+
+from datalab.datalab_session.data_operations.data_operation import BaseDataOperation
+from datalab.datalab_session.file_utils import create_fits, create_jpgs
+from datalab.datalab_session.s3_utils import save_fits_and_thumbnails
+
+log = logging.getLogger()
+log.setLevel(logging.INFO)
+
+
+class Normalization(BaseDataOperation):
+
+    @staticmethod
+    def name():
+        return 'Normalization'
+
+    @staticmethod
+    def description():
+        return """The normalize operation takes in 1..n input images and calculates each image's median value and divides every pixel by that value.
+
+The output is a normalized image. This operation is commonly used as a precursor step for flat removal."""
+
+    @staticmethod
+    def wizard_description():
+        return {
+            'name': Normalization.name(),
+            'description': Normalization.description(),
+            'category': 'image',
+            'inputs': {
+                'input_files': {
+                    'name': 'Input Files',
+                    'description': 'The input files to operate on',
+                    'type': 'file',
+                    'minimum': 1,
+                    'maximum': 999
+                }
+            }
+        }
+
+    def operate(self):
+
+        input = self.input_data.get('input_files', [])
+
+        log.info(f'Executing normalization operation on {len(input)} file(s)')
+
+        image_data_list = self.get_fits_npdata(input)
+        self.set_percent_completion(0.40)
+
+        output_files = []
+        for index, image in enumerate(image_data_list):
+            median = np.median(image)
+            normalized_image = image / median
+
+            fits_file = create_fits(self.cache_key, normalized_image)
+            large_jpg_path, small_jpg_path = create_jpgs(self.cache_key, fits_file)
+            output_file = save_fits_and_thumbnails(self.cache_key, fits_file, large_jpg_path, small_jpg_path, index=index)
+            output_files.append(output_file)
+
+            self.set_percent_completion(self.get_percent_completion() + .40 * (index + 1) / len(input))
+
+        output =  {'output_files': output_files}
+
+        self.set_output(output)
+        log.info(f'Normalization output: {self.get_output()}')